Uses of Class
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure

Packages that use Measure
de.jstacs.scoringFunctions.directedGraphicalModels Provides ScoringFunctions that are equivalent to directed graphical models. 
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures Provides the facilities to learn the structure of a BayesianNetworkScoringFunction
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a Bayesian tree using a number of measures to define a rating of structures 
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a permuted Markov model using a number of measures to define a rating of structures 
 

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels
 

Fields in de.jstacs.scoringFunctions.directedGraphicalModels declared as Measure
protected  Measure BayesianNetworkScoringFunction.structureMeasure
          Measure that defines the network structure.
 

Methods in de.jstacs.scoringFunctions.directedGraphicalModels that return Measure
 Measure BayesianNetworkScoringFunctionParameterSet.getMeasure()
          Returns the structure Measure defined by this set of parameters.
 

Constructors in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Measure
BayesianNetworkScoringFunction(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)
          Creates a new BayesianNetworkScoringFunction that has neither been initialized nor trained.
BayesianNetworkScoringFunctionParameterSet(AlphabetContainer alphabet, int length, double ess, boolean plugInParameters, Measure structureMeasure)
          Creates a new BayesianNetworkScoringFunctionParameterSet with pre-defined parameter values.
 

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
 

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
 class InhomogeneousMarkov
          Class for a network structure of a BayesianNetworkScoringFunction that is an inhomogeneous Markov model.
 

Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures that return Measure
 Measure Measure.clone()
           
 

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
 

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
 class BTExplainingAwayResidual
          Structure learning Measure that computes a maximum spanning tree based on the explaining away residual and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a BayesianNetworkScoringFunction .
 class BTMutualInformation
          Structure learning Measure that computes a maximum spanning tree based on mutual information and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a BayesianNetworkScoringFunction .
 

Uses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures
 

Subclasses of Measure in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures
 class PMMExplainingAwayResidual
          Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on the explaining away residual.
 class PMMMutualInformation
          Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on mutual information.